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In this paper, a new descriptor selection method for selecting an optimalcombination of important descriptors of sulfonamide derivatives data, namedself tuned reweighted sampling (STRS), is developed. descriptors are defined asthe descriptors with large absolute coefficients in a multivariate linearregression model such as partial least squares(PLS). In this study, theabsolute values of regression coefficients of PLS model are used as an indexfor evaluating the importance of each descriptor Then, based on the importancelevel of each descriptor, STRS sequentially selects N subsets of descriptorsfrom N Monte Carlo (MC) sampling runs in an iterative and competitive manner.In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomlyselected to establish a regresson model. Next, based on the regressioncoefficients, a two-step procedure including rapidly decreasing function (RDF)based enforced descriptor selection and self tuned sampling (STS) basedcompetitive descriptor selection is adopted to select the importantdescriptorss. After running the loops, a number of subsets of descriptors areobtained and root mean squared error of cross validation (RMSECV) of PLS modelsestablished with subsets of descriptors is computed. The subset of descriptorswith the lowest RMSECV is considered as the optimal descriptor subset. Theperformance of the proposed algorithm is evaluated by sulfanomide derivativedataset. The results reveal an good characteristic of STRS that it can usuallylocate an optimal combination of some important descriptors which areinterpretable to the biologically of interest. Additionally, our study showsthat better prediction is obtained by STRS when compared to full descriptor setPLS modeling, Monte Carlo uninformative variable elimination (MC-UVE).

Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity